importance: Calculate Importance for VarPro and UVarPro Objects

In varPro: Model-Independent Variable Selection via the Rule-Based Variable Priority

Calculate Importance for VarPro and UVarPro Objects

Description

Calculates variable importance from compatible objects such as varpro and uvarpro.

Usage

importance(x, ...)

## S3 method for class 'varpro'
importance(x, local.std = TRUE, y.external = NULL,
  cutoff = 0.79, trim = 0.1, plot.it = FALSE, conf = TRUE, sort = TRUE,
  ylab = if (conf) "Importance" else "Standardized Importance",
  max.rules.tree, max.tree,
  ...)

## S3 method for class 'uvarpro'
importance(x, local.std = FALSE, y.external = NULL,
  cutoff = 0.79, trim = 0.1, plot.it = FALSE, conf = TRUE, sort = TRUE,
  ylab = if (conf) "Importance" else "Standardized Importance",
  max.rules.tree, max.tree,
  ...)

## S3 method for class 'rhf'
importance(x, local.std = TRUE, y.external = NULL,
  cutoff = 0.79, trim = 0.1, plot.it = FALSE, conf = TRUE, sort = TRUE,
  ylab = if (conf) "Importance" else "Standardized Importance",
  max.rules.tree, max.tree,
  ...)

Arguments

x	A varpro, uvarpro or rhf object.
local.std	Logical. If TRUE, uses locally standardized importance values. Ignored for uvarpro objects.
y.external	Optional user-supplied response vector. Must match the expected dimension and outcome family. Ignored for uvarpro objects.
cutoff	Threshold used to highlight significant variables in the importance plot. Applies only when plot.it = TRUE.
trim	Windsorization trim value used to robustify the mean and standard deviation calculations.
plot.it	Logical. If TRUE, generates a plot of importance values.
conf	Logical. If TRUE, displays importance values with standard errors as a boxplot (providing an informal confidence region). If FALSE, plots standardized importance values.
sort	Logical. If TRUE, sorts results in decreasing order of importance.
ylab	Character string specifying the y-axis label.
max.rules.tree	Optional. Maximum number of rules per tree. Defaults to the value stored in the fitted object if unspecified.
max.tree	Optional. Maximum number of trees used for rule extraction. Defaults to the value from the fitted object if unspecified.
...	Additional arguments passed to internal methods.

Details

This page documents the public importance() generic together with the methods for varpro and uvarpro objects.

The supervised varpro method calculates standardized importance values for identifying and ranking variables. Optionally, graphical output is provided, including confidence-style boxplots.

Value

Invisibly, a table summarizing the results. Contains mean importance mean, the standard deviation std, and standardized importance z.

For classification, conditional z tables are additionally provided, where the z standardized importance values are conditional on the class label.

See cv.varpro for a data-driven cross-validation method for selecting the cutoff value, cutoff, in supervised varpro analyses.

Author(s)

Min Lu and Hemant Ishwaran

References

Lu, M. and Ishwaran, H., (2024). Model-independent variable selection via the rule-based variable priority. arXiv e-prints, pp.arXiv-2409.

See Also

cv.varpro varpro uvarpro

Examples

## ------------------------------------------------------------
## toy example - needed to pass CRAN test
## ------------------------------------------------------------

## mtcars regression
o <- varpro(mpg ~ ., mtcars, ntree = 1)
imp <- importance(o, local.std = FALSE)
print(imp)



## ------------------------------------------------------------
## iris example
## ------------------------------------------------------------

## apply varpro to the iris data
o <- varpro(Species ~ ., iris, max.tree = 5)

## print/plot the results
imp <- importance(o, plot.it = TRUE)
print(imp)

## ------------------------------------------------------------
## boston housing: regression
## ------------------------------------------------------------

data(BostonHousing, package = "mlbench")

## call varpro
o <- varpro(medv~., BostonHousing)

## extract importance values
imp <- importance(o)
print(imp)

## plot the results
imp <- importance(o, plot.it = TRUE)
print(imp)



## ------------------------------------------------------------
## illustrates y-external: regression example
## ------------------------------------------------------------

## friedman1 - standard application of varpro
d <- data.frame(mlbench::mlbench.friedman1(250),noise=matrix(runif(250*10,-1,1),250))
o <- varpro(y~.,d)
print(importance(o))

## importance using external rf predictor
print(importance(o,y.external=randomForestSRC::rfsrc(y~.,d)$predicted.oob))

## importance using external lm predictor
print(importance(o,y.external=lm(y~.,d)$fitted))

## importance using external randomized predictor
print(importance(o,y.external=sample(o$y)))

## ------------------------------------------------------------
## illustrates y-external: classification example
## ------------------------------------------------------------

## iris - standard application of varpro
o <- varpro(Species~.,iris)
print(importance(o))

## importance using  external rf predictor
print(importance(o,y.external=randomForestSRC::rfsrc(Species~.,iris)$class.oob))

## importance using  external randomized predictor
print(importance(o,y.external=sample(o$y)))

## ------------------------------------------------------------
## illustrates y-external: survival
## ------------------------------------------------------------
data(pbc, package = "randomForestSRC")
o <- varpro(Surv(days, status)~., pbc)
print(importance(o))

## importance using  external rsf predictor
print(importance(o,y.external=randomForestSRC::rfsrc(Surv(days, status)~., pbc)$predicted.oob))

## importance using  external randomized predictor
print(importance(o,y.external=sample(o$y)))

varPro documentation built on Sept. 22, 2026, 5:09 p.m.